A real-time system using deep learning to detect and track ureteral orifices during urinary endoscopy.

Journal: Computers in biology and medicine
Published Date: Nov 12, 2020

Abstract

BACKGROUND AND OBJECTIVE: To automatically identify and locate various types and states of the ureteral orifice (UO) in real endoscopy scenarios, we developed and verified a real-time computer-aided UO detection and tracking system using an improved real-time deep convolutional neural network and a robust tracking algorithm.

Authors

  • Dingyi Liu
  • Xin Peng
  • Xiaoqing Liu
  • Yiming Li
    Department of Cardiology, West China Hospital, Sichuan University, Chengdu 610041, China.
  • Yiming Bao
    BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.
  • Jianwei Xu
    School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
  • Xianzhang Bian
    Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200240, China.
  • Wei Xue
    School of Information Science and Technology, Nanjing Agricultural University, Nanjing 210095, Jiangsu, China.
  • Dahong Qian

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